from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 2s

KMeans_short: 0h 0m 3s

daal4py_LogisticRegression: 0h 0m 4s

daal4py_KMeans_tall: 0h 0m 8s

Ridge: 0h 0m 11s

LogisticRegression: 0h 0m 21s

KMeans_tall: 0h 0m 25s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 29s

KNeighborsClassifier_kd_tree: 0h 2m 45s

daal4py_KNeighborsClassifier: 0h 2m 48s

xgboost: 0h 5m 9s

catboost_symmetric: 0h 5m 9s

lightgbm: 0h 5m 10s

HistGradientBoostingClassifier: 0h 5m 13s

catboost_lossguide: 0h 5m 49s

KNeighborsClassifier: 0h 34m 8s

total: 1h 8m 2s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1033-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.5",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.749	0.000	1	100	NaN	NaN	0.502	0.000	0.277	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.023	0.311	0.000	0.023	1	100	0.934	0.716	1.999	0.054	11.517	0.347	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.211	0.005	0.000	0.211	1	100	0.000	1.000	0.086	0.002	2.460	0.078	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.129	0.000	6.184	0.000	-1	1	NaN	NaN	0.483	0.000	0.268	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.107	0.430	0.000	0.025	-1	1	0.710	0.819	1.981	0.032	12.676	0.297	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.198	0.026	0.000	0.198	-1	1	0.000	0.000	0.086	0.002	2.299	0.308	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.137	0.000	5.825	0.000	1	5	NaN	NaN	0.505	0.000	0.272	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	23.133	0.224	0.000	0.023	1	5	0.803	0.819	2.058	0.031	11.243	0.200	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.213	0.002	0.000	0.213	1	5	1.000	0.000	0.086	0.002	2.473	0.052	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.000	6.153	0.000	-1	5	NaN	NaN	0.484	0.000	0.268	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.087	0.000	0.000	0.035	-1	5	0.803	0.936	2.067	0.047	16.973	0.382	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.201	0.023	0.000	0.201	-1	5	1.000	1.000	0.085	0.001	2.370	0.276	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.129	0.000	6.219	0.000	1	1	NaN	NaN	0.483	0.000	0.266	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.360	0.046	0.000	0.013	1	1	0.710	0.716	1.952	0.018	6.845	0.066	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.206	0.004	0.000	0.206	1	1	0.000	1.000	0.086	0.002	2.380	0.070	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.132	0.000	6.064	0.000	-1	100	NaN	NaN	0.487	0.000	0.271	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	34.858	0.000	0.000	0.035	-1	100	0.934	0.936	2.040	0.015	17.089	0.126	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.206	0.019	0.000	0.206	-1	100	0.000	1.000	0.087	0.002	2.362	0.221	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.063	0.000	0.254	0.000	1	100	NaN	NaN	0.111	0.000	0.569	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.571	0.151	0.000	0.021	1	100	0.993	0.976	0.301	0.006	68.365	1.376	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.001	0.000	0.030	1	100	1.000	1.000	0.006	0.000	5.126	0.349	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.267	0.000	-1	1	NaN	NaN	0.114	0.000	0.528	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	22.343	0.142	0.000	0.022	-1	1	0.982	0.981	0.303	0.005	73.801	1.347	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.002	0.000	0.021	-1	1	1.000	1.000	0.006	0.000	3.552	0.455	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.062	0.000	0.260	0.000	1	5	NaN	NaN	0.114	0.000	0.539	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.289	0.363	0.000	0.020	1	5	0.991	0.981	0.304	0.005	66.635	1.581	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.001	0.000	0.030	1	5	1.000	1.000	0.006	0.001	5.160	0.529	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.070	0.000	0.227	0.000	-1	5	NaN	NaN	0.112	0.000	0.630	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.752	0.000	0.000	0.032	-1	5	0.991	0.981	0.363	0.009	87.431	2.206	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.038	0.004	0.000	0.038	-1	5	1.000	1.000	0.006	0.000	6.408	0.752	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.265	0.000	1	1	NaN	NaN	0.116	0.000	0.523	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.333	0.085	0.000	0.010	1	1	0.982	0.976	0.309	0.006	33.453	0.670	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.006	0.001	2.368	0.229	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.063	0.000	0.254	0.000	-1	100	NaN	NaN	0.117	0.000	0.537	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.734	0.000	0.000	0.032	-1	100	0.993	0.981	0.362	0.005	87.594	1.315	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.037	0.003	0.000	0.037	-1	100	1.000	1.000	0.006	0.000	6.157	0.668	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.273	0.000	0.024	0.000	1	1	NaN	NaN	0.742	0.000	4.409	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.762	0.006	0.000	0.001	1	1	0.955	0.972	0.194	0.001	3.924	0.040	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	3.296	1.508	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.221	0.000	0.025	0.000	-1	5	NaN	NaN	0.755	0.000	4.267	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.856	0.011	0.000	0.001	-1	5	0.966	0.965	0.108	0.002	7.950	0.163	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.000	0.000	12.210	5.251	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.249	0.000	0.025	0.000	1	5	NaN	NaN	0.730	0.000	4.452	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.468	0.004	0.000	0.001	1	5	0.966	0.973	0.578	0.006	2.539	0.026	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.001	0.000	2.428	0.944	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.169	0.000	0.025	0.000	-1	100	NaN	NaN	0.759	0.000	4.175	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.884	0.021	0.000	0.003	-1	100	0.966	0.965	0.109	0.001	26.412	0.334	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.009	0.001	0.000	0.009	-1	100	1.000	1.000	0.000	0.000	29.687	14.637	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.239	0.000	0.025	0.000	1	100	NaN	NaN	0.728	0.000	4.450	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.932	0.087	0.000	0.005	1	100	0.966	0.972	0.194	0.004	25.474	0.671	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.001	0.000	0.006	1	100	1.000	1.000	0.000	0.000	14.004	6.497	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.185	0.000	0.025	0.000	-1	1	NaN	NaN	0.742	0.000	4.291	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.451	0.005	0.000	0.000	-1	1	0.955	0.973	0.578	0.009	0.781	0.015	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	1	1.000	1.000	0.001	0.000	4.355	1.916	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.856	0.000	0.019	0.000	1	1	NaN	NaN	0.499	0.000	1.715	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	1	1	0.975	0.984	0.001	0.000	19.201	5.807	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.563	3.857	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.865	0.000	0.018	0.000	-1	5	NaN	NaN	0.511	0.000	1.694	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.001	0.001	0.000	-1	5	0.987	0.968	0.001	0.000	30.964	10.894	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	23.020	17.766	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.871	0.000	0.018	0.000	1	5	NaN	NaN	0.510	0.000	1.708	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	1	5	0.987	0.984	0.007	0.001	3.238	0.419	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	3.859	3.491	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.879	0.000	0.018	0.000	-1	100	NaN	NaN	0.516	0.000	1.704	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.048	0.001	0.000	0.000	-1	100	0.985	0.968	0.001	0.001	46.163	31.122	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	24.484	17.712	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.871	0.000	0.018	0.000	1	100	NaN	NaN	0.515	0.000	1.692	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.057	0.003	0.000	0.000	1	100	0.985	0.984	0.001	0.000	49.098	13.614	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.947	3.970	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.881	0.000	0.018	0.000	-1	1	NaN	NaN	0.513	0.000	1.718	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.001	0.001	0.000	-1	1	0.975	0.984	0.011	0.004	2.221	0.816	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	19.286	14.069	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.683	0.000	0.703	0.000	random	NaN	30	NaN	0.509	0.0	1.341	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.000	0.326	0.000	random	0.000	30	0.001	0.000	0.0	6.831	3.066	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.866	6.487	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.623	0.000	0.770	0.000	k-means++	NaN	30	NaN	0.450	0.0	1.385	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.314	0.000	k-means++	0.001	30	0.001	0.000	0.0	9.691	5.956	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	11.023	7.035	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	7.149	0.000	3.357	0.000	random	NaN	30	NaN	3.097	0.0	2.308	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.001	10.593	0.000	random	0.002	30	0.002	0.000	0.0	8.090	5.465	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.001	0.015	0.002	random	1.000	30	1.000	0.000	0.0	10.872	6.854	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.856	0.000	3.501	0.000	k-means++	NaN	30	NaN	2.896	0.0	2.368	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	13.090	0.000	k-means++	0.002	30	0.002	0.000	0.0	6.054	2.111	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.016	0.002	k-means++	1.000	30	1.000	0.000	0.0	10.932	6.219	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.254	0.0	0.013	0.000	k-means++	NaN	20	NaN	0.103	0.0	2.471	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.160	0.000	k-means++	0.001	20	-0.002	0.001	0.0	2.968	0.634	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.606	6.597	See	See
3	KMeans_short	fit	10000	10000	2	20	0.097	0.0	0.033	0.000	random	NaN	20	NaN	0.036	0.0	2.677	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.158	0.000	random	0.000	20	0.002	0.001	0.0	2.556	0.917	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.544	5.849	See	See
6	KMeans_short	fit	10000	10000	100	20	0.653	0.0	0.245	0.000	k-means++	NaN	20	NaN	0.388	0.0	1.683	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.903	0.000	k-means++	0.296	20	0.322	0.001	0.0	2.153	0.331	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.010	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.665	4.095	See	See
9	KMeans_short	fit	10000	10000	100	20	0.238	0.0	0.673	0.000	random	NaN	20	NaN	0.152	0.0	1.563	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.805	0.000	random	0.318	20	0.322	0.001	0.0	2.115	0.339	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.010	0.002	random	1.000	20	1.000	0.000	0.0	7.506	3.566	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	12.331	0.0	[-0.09568393]	0.000	NaN	NaN	NaN	NaN	NaN	1.961	0.0	6.288	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[50.26502232]	0.000	NaN	NaN	NaN	NaN	0.51	0.000	0.0	0.764	0.358	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.2382704]	0.000	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.359	0.322	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.894	0.0	[2.32663709]	0.001	NaN	NaN	NaN	NaN	NaN	0.739	0.0	1.210	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[112.34882669]	0.000	NaN	NaN	NaN	NaN	0.28	0.003	0.0	0.538	0.105	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[19.323492]	0.000	NaN	NaN	NaN	NaN	1.00	0.001	0.0	0.140	0.094	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.190	0.0	0.421	0.0	NaN	NaN	NaN	0.189	0.0	1.006	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	7.982	0.0	NaN	NaN	0.104	0.017	0.0	0.588	0.023	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	0.930	0.0	NaN	NaN	NaN	0.000	0.0	0.641	0.574	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.556	0.0	0.514	0.0	NaN	NaN	NaN	0.243	0.0	6.407	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	5.344	0.0	NaN	NaN	1.000	0.000	0.0	0.648	0.453	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.010	0.0	NaN	NaN	NaN	0.000	0.0	0.660	0.561	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw fit times vs. accuracy scores

Raw predict times vs. accuracy scores

Smoothed HPO Curves

The shaded areas represent boostrapped quartiles.

Speedup barplots

Speedup curves